Harvard University: Pursuing Excellence in a World-class Environment

Over the past two years, I have navigated between mathematics, computer science and biology at École Polytechnique. Throughout this journey, I have continually tried to create links between these three areas, imagining how techniques taken from one subject could be applied to another. I quickly realized that the field of Computational Science and Engineering was what I was looking for, combining an interdisciplinary, application-oriented approach with computer simulation capabilities to tackle real-world problems. While delving into Harvard supplemental essays, I witnessed the immense creativity and intellectual depth demonstrated by applicants, motivating me to articulate my own unique perspectives and aspirations that align with Harvard's esteemed academic community. However in this Harvard admission essay I will explain my academic interests and background, then explain why I want to be a part of Harvard University. 

Last year, I came across Markov chains while studying for a third semester probability course. I found the scope of their potential applications very exciting. Simulating different types of Markov chains on Jupyter notebooks, poring over research articles to explore new stochastic models, implementing MCMC algorithms to retrieve a given invariant distribution allowed me to catch a glimpse of the beauty of probability, and made me want to learn more. I thus took advantage of studying at one of the leading institutions in mathematics to study this subject more in depth through a two-month internship under the supervision of Assistant Professor Giovanni Conforti of the Department of Applied Mathematics (CMAP). During this internship, I became interested in the convergence speed of Markov chains to their invariant distributions and studied various ways to approximate it, using, for instance, Ollivier’s Ricci curvature. Additionally, I highly enjoyed studying bounds on the estimation error of MCMC algorithms as well as digging into the numerous applications of Markov chains in biology. I wrote my Bachelor Thesis on the work that I did during these two months and successfully defended it on October 25. At Harvard, I would look forward to studying class AM 207 to broaden my knowledge on stochastic methods. In addition, I would be very interested in joining Professor Amir’s research group to further explore the use of stochastic processes in the study of living systems. The research on uncertainty quantification and machine learning conducted at CSElab also closely aligns with my learning objectives. The study of statistical inference and the development of deep reinforcement learning algorithms for the Korali Project are of particular interest to me.

Last semester, I explored machine learning in depth with Professor Jesse Read's course at École Polytechnique. While working on a project for this course, I taught myself reinforcement learning methods. I am currently working on a project based on both the machine learning techniques I learned last semester and the notions of optimal transport I acquired during my internship with Professor Giovanni Conforti. I am interested in the geodesic path between two images, given by the most efficient way of gradually transforming one image into another. My goal is to use this technique as an approach for pattern recognition in medical imaging. I believe that the Harvard Master’s program in Computational Science and Engineering would constitute an ideal opportunity for me to pursue the aforementioned project, by giving me access to advanced classes in computational science and machine learning, for example AM 328 and CS 387. I am particularly attracted to the interdisciplinary nature of the program overall. My experience at École Polytechnique has taught me that communicating across disciplines is paramount in science. Participating in research groups where diverse departments are represented alongside taking courses in various fields will facilitate my development as a versatile computational scientist.

At École Polytechnique, while being a member of several student committees — I received a special distinction on all my transcripts for my dedication to the university community — I achieved a cumulative GPA of 4.15 in the first two years of my undergraduate program, thereby demonstrating my strong academic aptitude. For being one of the top performing students in my class, I was also awarded an Excellence Scholarship for three consecutive years. Pursuing a double major in mathematics and computer science has given me both considerable computing skills and a solid mathematical foundation. The advanced level and selective nature of my program has additionally allowed me to attend graduate level classes in Numerical Programming at the Technical University of Munich during this semester.

In the past few years, I have gained considerable teaching experience, working as a tutor at Schülerhilfe GmbH Co. KG in Berlin during my gap year and giving weekly math lessons to high school students during my first year at École Polytechnique. I would be keen to continue teaching throughout my graduate studies at Harvard University, via a Teaching Fellowship.

In conclusion, I look forward to pursuing my academic career at the Harvard John A. Paulson School of Engineering and Applied Sciences, where I can continue to learn new mathematical techniques for modeling complex systems and expand my research experience with, among others, the Capstone project (AC 297r) and the thesis project part of the ME program. I am confident that I have the suitable mixture of curiosity, drive and intellectual rigor that will allow me to thrive at Harvard University and actively contribute to its academic, research and social life. This Master's program in Computational Science and Engineering will not only deepen my knowledge in the areas I am most interested in, namely applied probability, computational mathematics and machine learning, but it will also lead me towards my goal of pursuing a research-oriented career in the computational sciences.